Package index • MOSAIC

Core Workflow

Main functions for running MOSAIC simulations

run_simulation(): Run the cholera transmission model
run_MOSAIC() run_mosaic(): Run MOSAIC Calibration Workflow
render_MOSAIC_figures(): Render all MOSAIC figures from a finished run directory
make_simulation_config(): Create a Simulation Configuration File
sample_parameters(): Sample Parameters from Prior Distributions
check_dependencies(): Check Installed R and Python Dependencies for MOSAIC
install_dependencies(): Install R and Python Dependencies for MOSAIC
set_root_directory(): Set Root Directory
get_paths(): Generate Directory Paths for the MOSAIC Project

Configuration & Control

Functions for configuring MOSAIC execution settings

mosaic_control_defaults(): Build Complete MOSAIC Control Structure
mosaic_io_presets(): Get Pre-configured I/O Settings

Model Execution

Functions starting with run_, make_, or create_

run_MOSAIC() run_mosaic(): Run MOSAIC Calibration Workflow
run_WHO_annual_data_app(): Run Shiny Application for Visualizing WHO Annual Cholera Data in AFRO Countries
run_fit_sandbox(): Deterministic Fit-Diagnostic Sandbox: One Simulation with Parameter Overrides
run_rolling_cv(): Rolling-Window Forecast Validation for the MOSAIC Transmission Model
run_simulation(): Run the cholera transmission model
make_forecast_cv_table(): Publication table for rolling-origin forecast cross-validation
make_lagged_data(): Create Lagged Versions of Data
make_mosaic_cluster(): Create a Reusable MOSAIC Parallel Cluster
make_mu_jt(): Build the daily reported-CFR matrix mu_jt from annual estimates
make_param_df(): Get Template for Parameter Values
make_simulation_config(): Create a Simulation Configuration File
create_sampling_args(): Create Sampling Arguments for Common Patterns

Parameter Estimation

Estimation and fitting functions

est_CFR_hierarchical(): Estimate the time-varying reported case fatality ratio (mu_jt) from WHO annual data
est_WASH_coverage(): Estimate, Visualize, and Impute WASH Coverage and Correlation with Cholera Incidence
est_demographic_rates(): Estimate Daily Demographic Rates from UN World Population Prospects Data
est_epidemic_peaks(): Estimate Epidemic Peaks from Cholera Time Series Data
est_immune_decay_vaccine(): Plot Vaccine Immune Decay
est_initial_E_I(): Estimate Initial E and I Compartments from Surveillance Data
est_initial_E_I_location(): Estimate E and I Compartments for a Single Location
est_initial_R(): Estimate Initial R Compartment from Historical Cholera Surveillance
est_initial_R_location(): Estimate Initial R Compartment for Single Location
est_initial_S(): Estimate Initial S Compartment from Other Compartment Priors
est_initial_V1_V2(): Estimate V1/V2 Initial-Condition Beta Priors from OCV Campaign History
est_kappa_prior(): Estimate Prior Distribution for Environmental Half-Saturation Constant (kappa)
est_mobility(): Fit Mobility Model Using Flight Data and Distance Matrices
est_nb_dispersion(): Estimate negative-binomial dispersion from surveillance observations
est_overland_tau_prior(): Build a per-country overland departure-rate prior for MOSAIC calibration
est_seasonal_dynamics(): Estimate Seasonal Dynamics for Cholera and Precipitation Using Daily Fourier Series
est_suitability(): Estimate Environmental Suitability (psi) for Cholera Transmission
est_symptomatic_prop(): Estimate and Visualize Symptomatic Proportion Data
est_vaccination_rate(): Estimate OCV Vaccination Rates
est_vaccine_effectiveness(): Estimate Vaccine Effectiveness Decay Parameters
est_zeta_1_prior(): Estimate Prior Distribution for Symptomatic Shedding Rate (zeta_1)
est_zeta_2_prior(): Estimate Prior Distribution for Asymptomatic Shedding Rate (zeta_2)
est_zeta_ratio_prior(): Estimate Prior Distribution for the Shedding Ratio (zeta_ratio = zeta_1 / zeta_2)
fit_beta_from_ci(): Fit Beta Distribution from Mode and 95% Confidence Intervals
fit_beta_safe(): Helper Function to Safely Fit Beta Distribution
fit_beta_with_variance_inflation_R(): Helper Function to Fit Beta Distribution with Variance Inflation for est_initial_R
fit_gamma_from_ci(): Fit Gamma Distribution from Mode and 95% Confidence Intervals
fit_gompertz_from_ci(): Fit Gompertz Distribution from Mode and Probability Interval
fit_lognormal_from_ci(): Fit Lognormal Distribution from Mode and 95% Confidence Intervals
fit_normal_from_ci(): Fit Normal Distribution from Mode and 95% Confidence Intervals
fit_truncnorm_from_ci(): Fit Truncated Normal Distribution from Mode and 95% Confidence Intervals
fit_uniform_from_ci(): Fit Uniform Distribution from Mode and 95% Confidence Intervals
sample_from_prior(): Sample from Prior Distribution (Simplified)
sample_parameters(): Sample Parameters from Prior Distributions

Calculations

Calculation functions

calc_Reff(): Route-decomposed Cori effective reproductive number from ensemble trajectories
calc_affine_normalization(): Affine Normalization of a Numeric Vector (Zero-Centered Min-Max Scaling)
calc_bias_ratio(): Bias Ratio Between Estimated and Observed Series
calc_bookend_batch_size(): Calculate Batch Size for Bookend Strategy
calc_convergence_diagnostics(): Calculate comprehensive convergence diagnostics with status indicators
calc_diffusion_matrix_pi(): Compute Diffusion Matrix Based on Gravity Model
calc_fit_diagnostics(): Fit Diagnostics: Bias, Shape, and Variance for a Single Observed/Predicted Series
calc_is_diagnostics(): Exact importance-sampling diagnostics for BFRS draws
calc_kl_divergence() calculate_kl_divergence(): Calculate Kullback-Leibler Divergence Between Two Distributions
calc_log_likelihood(): Dispatcher for multiple distribution log-likelihood functions
calc_log_likelihood_beta(): Calculate log-likelihood for Beta-distributed proportions
calc_log_likelihood_binomial(): Calculate log-likelihood for Binomial-distributed data
calc_log_likelihood_deaths_integrated(): Deaths log-likelihood with the reported CFR integrated out
calc_log_likelihood_gamma(): Calculate log-likelihood for Gamma-distributed data
calc_log_likelihood_negbin(): Calculate log-likelihood for Negative Binomial-distributed count data
calc_log_likelihood_normal(): Calculate log-likelihood for Normally-distributed continuous data
calc_log_likelihood_poisson(): Calculate log-likelihood for Poisson-distributed count data
calc_log_mean_exp(): Stable log-mean-exp computation
calc_mobility_flux(): Compute the model-implied mobility flux from a run configuration
calc_model_R2(): Coefficient of Determination (R-squared) Between Observed and Estimated Series
calc_model_agreement_index(): Agreement index A based on entropy of weights
calc_model_convergence_status(): Assemble the model convergence-status table
calc_model_cor(): Pearson (or Rank) Correlation Between Observed and Estimated Series
calc_model_cvw(): Coefficient of variation of (retained) weights
calc_model_ensemble(): Compute Weighted Ensemble Predictions from Multiple Parameter Sets
calc_model_ess(): Calculate Effective Sample Size (ESS)
calc_model_ess_parameter(): Calculate Parameter-Specific ESS
calc_model_likelihood(): Compute the total model likelihood
calc_model_parameter_sensitivity(): Compute Parameter Sensitivity Ranking (R2-HSIC)
calc_model_posterior_distributions(): Calculate Model Posterior Distributions from Quantiles
calc_model_posterior_quantiles(): Calculate Prior and Posterior Quantiles with KL Divergence
calc_model_weights_gibbs(): Gibbs-Posterior Model Weights
calc_psi_star(): Calibrate Daily Suitability on the Logit Scale (vector; optional causal EWMA + time offset + NA filling)
calc_spatial_correlation(): Calculate spatial correlation between two locations
calc_spatial_correlation_matrix(): Build the full spatial-correlation matrix across all locations
calc_spatial_hazard(): Calculate spatial importation hazard
calc_weighted_mode(): Weighted mode estimation using kernel density
add_reproductive_numbers(): Add route-decomposed Cori R_eff to an existing MOSAIC model output directory

Data Processing

Processing and downloading data

process_AI_cholera_data(): Process AI-mined Weekly Cholera Surveillance Data into the MOSAIC Per-Source Schema
process_CFR_data(): Process Cholera Data to Calculate Aggregated Case Fatality Ratios and Fit Beta Distributions
process_EMDAT_data(): Process EM-DAT Flood and Tropical-Cyclone Events Into Country-Week Panels
process_GTFCC_vaccination_data(): Process GTFCC Vaccination Data for MOSAIC Model
process_IDMC_data(): Process IDMC Internal Displacement Events Into Country-Week Panels
process_JHU_weekly_data(): Process Weekly Cholera Data from JHU: Convert to MOSAIC Format
process_OAG_data(): Process and Save OAG Flight Data for Africa
process_SUPP_weekly_data(): Process supplementary cholera outbreak data from raw SUPP directory
process_UNICEF_malnutrition_data(): Process UNICEF Child Malnutrition Data
process_UN_demographics_data(): Process UN World Prospects Data for African Countries
process_WB_GDP_data(): Process World Bank GDP data
process_WB_population_density_data(): Process World Bank Population Density Data
process_WB_poverty_ratio_data(): Process World Bank Poverty Ratio Data
process_WB_urban_population_data(): Process World Bank Urban Population Proportion Data
process_WHO_annual_data(): Download, Process, and Save Historical Annual WHO Cholera Data for Africa
process_WHO_vaccination_data(): Process Vaccination Data for MOSAIC Model
process_WHO_weekly_data(): Process Weekly Cholera Data: Convert Country Names to ISO3 and Filter by WHO AFRO Region
process_cholera_surveillance_data(): Process Combined Weekly and Daily Cholera Surveillance Data with Truly Square Data Structure
process_country_similarity_data(): Process Country Similarity Data for African Countries
process_enso_data(): Process ENSO Data from enso-data Repository
process_mobility_od_data(): Fuse four bilateral mobility sources into a connectivity structure
process_open_meteo_data(): Process Open-Meteo Climate Data from open-meteo-pipeline Repository
download_EMDAT_data(): Download EM-DAT disaster events from the CRED GraphQL API
download_IDMC_data(): Download IDMC Internal Displacement Updates (IDU) from the HDX mirrors
download_UN_WPP_data(): Download UN World Population Prospects demographic indicators
download_WB_data(): Download World Bank indicators from the Indicators API
download_africa_shapefile(): Download and Save Africa Shapefile
download_all_country_shapefiles(): Download and Save Shapefiles for All African Countries
download_country_DEM(): Download 1km DEM Raster for Multiple Countries and Save to Files
download_mobility_od_sources(): Download the bilateral mobility sources used to build the fused OD structure
combine_vaccination_data(): Combine WHO and GTFCC Vaccination Data

Visualization

Plotting functions

plot_CFR_by_country(): Plot Case Fatality Ratios, Total Cases, and Beta Distributions by Country
plot_CFR_hierarchical(): Plot Hierarchical CFR Model Results
plot_ENSO_data(): Plot ENSO and IOD Data
plot_Reff(): Plot the route-decomposed effective reproductive number over time
plot_africa_map(): Plot Africa Map with Cholera Outbreak Countries
plot_cases_binary(): Plot Binary Environmental Suitability Indicator with Case Data
plot_cholera_surveillance_data(): Plot Combined Cholera Surveillance Data
plot_climate_data(): Plot Weekly Climate Data for a Given Country and Save to File
plot_departure_tau(): Plot the model-implied departure probability (\(\tau_i\)) and daily travelers
plot_diffusion_pi(): Plot the model-implied diffusion connectivity matrix (\(\pi_{ij}\))
plot_effective_range(): Plot Effective Range Function
plot_epidemic_peaks(): Plot Epidemic Peaks from Detection Results
plot_forecast_cv_grid(): Publication timeseries grid for rolling-origin forecast cross-validation
plot_forecast_cv_skill(): Headline forecast-CV plot: per-country out-of-sample metric across origins
plot_generation_time(): Plot Generation Time as Gamma Distribution for Days and Weeks
plot_mobility(): Plot Mobility Data, Mobility Network, and Results
plot_mobility_flux_matrix(): Plot the model-implied daily mobility flux matrix (\(M_{ij}\))
plot_mobility_flux_network(): Plot the model-implied mobility flux network
plot_mobility_fused(): Publication figures for the fused overland OD connectivity model
plot_model_convergence_status(): Plot model convergence status table
plot_model_distributions(): Plot Multi-Method Parameter Distributions
plot_model_ensemble(): Plot Ensemble Predictions from a mosaic_ensemble Object
plot_model_likelihood(): Plot Model Likelihood Curve
plot_model_parameter_correlation(): Plot Posterior Parameter Correlation Heatmap
plot_model_parameter_sensitivity(): Plot Parameter Sensitivity Ranking (HSIC)
plot_model_parameters(): Plot Model Parameters vs Likelihood (Deprecated)
plot_model_posterior_quantiles(): Plot Model Posterior Quantiles
plot_model_posteriors_detail(): Plot Detailed Prior to Posterior Distributions
plot_model_ppc(): Plot Posterior Predictive Checks from Ensemble Predictions
plot_model_subset_optimization(): Plot Best-Subset Optimization Diagnostic from a mosaic_subset_optimization Object
plot_model_trajectories(): Plot comprehensive model trajectories for one location
plot_mosaic_country_map(): Plot MOSAIC Country Map
plot_mosaic_palette(): Display a Color Palette as Swatches
plot_psi_star_diagnostic(): Plot Raw vs Calibrated Environmental Suitability (psi vs psi*)
plot_recovery_duration(): Plot recovery durations and rates for symptomatic and asymptomatic infections
plot_rho_care_seeking_params(): Plot Rho (Care-Seeking Rate) Prior Estimation
plot_rolling_cv(): Presentation-quality plot of a rolling-origin forecast-validation artifact
plot_seasonal_clustering(): Plot Clustering of Countries Based on Seasonal Dynamics
plot_seasonal_transmission(): Plot Precipitation and Cholera Case Data with Fourier Series Fits
plot_seasonal_transmission_example(): Plot Seasonal Transmission Example with Grid Points
plot_shedding_rate(): Plot Shedding Rate as a Function of Environmental Suitability
plot_spatial_correlation_heatmap(): Heat-map of a spatial–correlation matrix (ggplot2)
plot_spatial_hazard(): Plot spatial importation hazard
plot_suitability_and_cases(): Plot Predicted Suitability and Reported Cholera Cases for a Specific Country
plot_suitability_by_country(): Plot Predicted Suitability for All Countries
plot_suspected_cases(): Plot Chi PPV Prior Distributions
plot_vaccination_data(): Plot Vaccination Data
plot_vaccination_maps(): Plot Vaccination Maps
plot_vaccine_effectiveness(): Plot Vaccine Effectiveness Decay and the Distributions Fitted to It
plot_vibrio_decay_rate(): Plot Vibrio Decay Rate as a Function of Environmental Suitability

Utility Functions

Helper and utility functions

get_WASH_data(): Download, Process, and Save WASH Coverage Data
get_WHO_vaccination_data(): Get WHO Vaccination Data
get_cases_binary(): Create Binary Environmental Suitability Indicator from Case Data
get_cases_binary_from_peaks(): Create Binary Environmental Suitability Indicator from Epidemic Peaks
get_centroid(): Calculate the Centroid of a Country from its Shapefile
get_country_shp(): Get a country shapefile from GeoBoundaries API
get_default_config(): Create Default Simulation Configuration
get_default_subset_tiers(): Get Default Subset Tiers for Post-Hoc Optimization
get_distance_matrix(): Build distance matrix from longitude/latitude coordinates
get_effective_aic_range(): Get Effective AIC Range for Gibbs Temperature Scaling
get_elevation(): Get Mean or Median Elevation from Downloaded DEM Data Using Country Boundaries
get_feature_set(): Resolve a named feature set to a covariate vector
get_generation_time_distribution(): Estimate the Generation Time Distribution for Days and Weeks
get_ggplot_legend(): Extract the Legend from a ggplot Object
get_greek_unicode(): Get Unicode Hex Key for a Greek Letter
get_immune_decay_data(): Get Immune Decay Data
get_location_config(): Get Location-Specific Configuration
get_location_priors(): Get Location-Specific Priors
get_lstm_sequence_splits(): Create Train/Validation Splits for LSTM Sequences
get_param_names(): Get Parameter Names from MOSAIC Objects
get_paths(): Generate Directory Paths for the MOSAIC Project
get_python_paths(): Get Python Environment Paths
get_rho_care_seeking_params(): Estimate Beta Prior for Rho (Care-Seeking Rate) and Save Parameter Data Frame
get_suspected_cases(): Get Chi PPV Parameters and Save Parameter Data Frame
get_symptomatic_prop_data(): Get Symptomatic Proportion Data
get_travel_time_matrix(): Build an overland least-cost travel-time matrix between countries
get_vaccine_effectiveness_data(): Get Vaccine Effectiveness Data
get_who_region(): Get WHO Region for ISO Country Codes
check_affine_normalization(): Check Affine Normalization (Zero-Centered Min-Max Scaling)
check_dependencies(): Check Installed R and Python Dependencies for MOSAIC
check_mosaic_data_freshness(): Report the freshness of every MOSAIC data input and output
check_mosaic_manual_inputs(): Report on the MOSAIC data sources that must be refreshed by hand
check_psi_amplitude(): Amplitude guard for post-correction suitability (psi)
check_python_env(): Check Python Environment for MOSAIC
check_sampled_parameter(): Check Sampled Parameter Against Prior
convert_config_to_dataframe(): Convert Config to DataFrame
convert_config_to_matrix(): Convert Config to Numeric Vector for Matrix Storage
convert_country_to_iso(): Convert Country Names to ISO3 or ISO2 Country Codes
convert_iso_codes(): Convert ISO3 to ISO2 or ISO2 to ISO3 Country Codes
convert_iso_to_country(): Convert ISO2 or ISO3 Country Codes to Country Names
convert_matrix_to_config(): Convert Matrix Row to Config Object
read_hdf5_to_list(): Read an HDF5 File into an R List
read_json_to_list(): Read a JSON File into an R List
read_model_json(): Read priors or posteriors from JSON with consistent parsing
read_yaml_to_list(): Read a YAML File into an R List
write_json_or_gz(): Write a list to .json, .json.gz, or both – byte-equal by construction
write_list_to_hdf5(): Write an R list to an HDF5 file
write_list_to_json(): Write an R List to a JSON File
write_list_to_yaml(): Write an R List to a YAML File
write_model_json(): Standardized JSON I/O utilities for MOSAIC model files
write_trajectory_csv(): Write ensemble trajectory channels to per-location CSV
use_mosaic_env(): Use MOSAIC Python Environment
attach_mosaic_env(): Attach MOSAIC Python Environment
detach_mosaic_env(): Detach MOSAIC Python Environment
inv_logit(): Inverse Logit (Logistic) Transformation
logit(): Logit Transformation
log_msg(): Simple Logging Function with Timestamps

Distribution Functions

Gompertz distribution functions

dgompertz(): Gompertz Distribution Density Function
pgompertz(): Gompertz Distribution Cumulative Distribution Function
qgompertz(): Gompertz Distribution Quantile Function
rgompertz(): Generate Random Gompertz Variates

Package Data

Built-in datasets

config_default: Default Simulation Configuration
config_simulation_endemic: Endemic Simulation Configuration
config_simulation_epidemic: Epidemic Simulation Simulation Configuration
priors_default: Default prior distributions for MOSAIC model parameters
estimated_parameters: Estimated Parameters Inventory
epidemic_peaks: Cholera Epidemic Peaks Data
iso_codes_africa: ISO3 Country Codes for All African Countries
iso_codes_africa_central: ISO3 Country Codes for Central African Countries
iso_codes_africa_east: ISO3 Country Codes for East African Countries
iso_codes_africa_north: ISO3 Country Codes for North African Countries
iso_codes_africa_south: ISO3 Country Codes for Southern African Countries
iso_codes_africa_west: ISO3 Country Codes for West African Countries
iso_codes_mosaic: ISO3 Country Codes for MOSAIC Modeling Framework
iso_codes_ssa: ISO3 Country Codes for Sub-Saharan African Countries
iso_codes_who_afro: ISO3 Country Codes for WHO AFRO Region Countries

Internal & Other Functions

Other exported functions and internal helpers

MINFEAT_V7_3_FEATURE_SET: Named LSTM covariate feature sets for est_suitability()
MINFEAT_V7_4_FEATURE_SET: v7.4 LSTM covariate feature set (v7.3 + lean cyclone/drought hazard channels)
MOSAIC_E3_TAU_WEEKLY: Per-country overland departure rates from the E3 border-throughput evidence
MOSAIC_WB_INDICATORS: World Bank indicators MOSAIC consumes
add_reproductive_numbers(): Add route-decomposed Cori R_eff to an existing MOSAIC model output directory
attach_mosaic_env(): Attach MOSAIC Python Environment
backfill_weekly_case_gaps(): Backfill Short Reporting Gaps in a Weekly Case Series
batch_clean_json(): Batch clean multiple JSON files
calc_Reff(): Route-decomposed Cori effective reproductive number from ensemble trajectories
calc_affine_normalization(): Affine Normalization of a Numeric Vector (Zero-Centered Min-Max Scaling)
calc_bias_ratio(): Bias Ratio Between Estimated and Observed Series
calc_bookend_batch_size(): Calculate Batch Size for Bookend Strategy
calc_convergence_diagnostics(): Calculate comprehensive convergence diagnostics with status indicators
calc_diffusion_matrix_pi(): Compute Diffusion Matrix Based on Gravity Model
calc_fit_diagnostics(): Fit Diagnostics: Bias, Shape, and Variance for a Single Observed/Predicted Series
calc_is_diagnostics(): Exact importance-sampling diagnostics for BFRS draws
calc_kl_divergence() calculate_kl_divergence(): Calculate Kullback-Leibler Divergence Between Two Distributions
calc_log_likelihood(): Dispatcher for multiple distribution log-likelihood functions
calc_log_likelihood_beta(): Calculate log-likelihood for Beta-distributed proportions
calc_log_likelihood_binomial(): Calculate log-likelihood for Binomial-distributed data
calc_log_likelihood_deaths_integrated(): Deaths log-likelihood with the reported CFR integrated out
calc_log_likelihood_gamma(): Calculate log-likelihood for Gamma-distributed data
calc_log_likelihood_negbin(): Calculate log-likelihood for Negative Binomial-distributed count data
calc_log_likelihood_normal(): Calculate log-likelihood for Normally-distributed continuous data
calc_log_likelihood_poisson(): Calculate log-likelihood for Poisson-distributed count data
calc_log_mean_exp(): Stable log-mean-exp computation
calc_mobility_flux(): Compute the model-implied mobility flux from a run configuration
calc_model_R2(): Coefficient of Determination (R-squared) Between Observed and Estimated Series
calc_model_agreement_index(): Agreement index A based on entropy of weights
calc_model_convergence_status(): Assemble the model convergence-status table
calc_model_cor(): Pearson (or Rank) Correlation Between Observed and Estimated Series
calc_model_cvw(): Coefficient of variation of (retained) weights
calc_model_ensemble(): Compute Weighted Ensemble Predictions from Multiple Parameter Sets
calc_model_ess(): Calculate Effective Sample Size (ESS)
calc_model_ess_parameter(): Calculate Parameter-Specific ESS
calc_model_likelihood(): Compute the total model likelihood
calc_model_parameter_sensitivity(): Compute Parameter Sensitivity Ranking (R2-HSIC)
calc_model_posterior_distributions(): Calculate Model Posterior Distributions from Quantiles
calc_model_posterior_quantiles(): Calculate Prior and Posterior Quantiles with KL Divergence
calc_model_weights_gibbs(): Gibbs-Posterior Model Weights
calc_psi_star(): Calibrate Daily Suitability on the Logit Scale (vector; optional causal EWMA + time offset + NA filling)
calc_spatial_correlation(): Calculate spatial correlation between two locations
calc_spatial_correlation_matrix(): Build the full spatial-correlation matrix across all locations
calc_spatial_hazard(): Calculate spatial importation hazard
calc_weighted_mode(): Weighted mode estimation using kernel density
calibrate_psi_predictions(): Per-country logit-scale bias-correction of suitability (psi) predictions
check_affine_normalization(): Check Affine Normalization (Zero-Centered Min-Max Scaling)
check_dependencies(): Check Installed R and Python Dependencies for MOSAIC
check_mosaic_data_freshness(): Report the freshness of every MOSAIC data input and output
check_mosaic_manual_inputs(): Report on the MOSAIC data sources that must be refreshed by hand
check_psi_amplitude(): Amplitude guard for post-correction suitability (psi)
check_python_env(): Check Python Environment for MOSAIC
check_sampled_parameter(): Check Sampled Parameter Against Prior
clean_priors_json(): Convert array-wrapped JSON to clean format
combine_vaccination_data(): Combine WHO and GTFCC Vaccination Data
compile_rolling_cv_predictions(): Rebuild the rolling-CV predictions table from run directories
compile_suitability_data(): Compile Environmental Suitability Data for Cholera Transmission
config_default: Default Simulation Configuration
config_simulation_endemic: Endemic Simulation Configuration
config_simulation_epidemic: Epidemic Simulation Simulation Configuration
convert_config_to_dataframe(): Convert Config to DataFrame
convert_config_to_matrix(): Convert Config to Numeric Vector for Matrix Storage
convert_country_to_iso(): Convert Country Names to ISO3 or ISO2 Country Codes
convert_iso_codes(): Convert ISO3 to ISO2 or ISO2 to ISO3 Country Codes
convert_iso_to_country(): Convert ISO2 or ISO3 Country Codes to Country Names
convert_matrix_to_config(): Convert Matrix Row to Config Object
create_sampling_args(): Create Sampling Arguments for Common Patterns
detach_mosaic_env(): Detach MOSAIC Python Environment
detect_sampled_parameters_from_configs(): Detect Parameter Sampling Flags from Config
dgompertz(): Gompertz Distribution Density Function
disagg_annual_cases_to_daily(): Temporal Disaggregation of Annual Cases to Daily Resolution
download_EMDAT_data(): Download EM-DAT disaster events from the CRED GraphQL API
download_IDMC_data(): Download IDMC Internal Displacement Updates (IDU) from the HDX mirrors
download_UN_WPP_data(): Download UN World Population Prospects demographic indicators
download_WB_data(): Download World Bank indicators from the Indicators API
download_africa_shapefile(): Download and Save Africa Shapefile
download_all_country_shapefiles(): Download and Save Shapefiles for All African Countries
download_country_DEM(): Download 1km DEM Raster for Multiple Countries and Save to Files
download_mobility_od_sources(): Download the bilateral mobility sources used to build the fused OD structure
downscale_weekly_cholera_data(): Downscale Weekly Cholera Data to Daily Frequency
downscale_weekly_values(): Downscale weekly values to a daily time series
epidemic_peaks: Cholera Epidemic Peaks Data
est_CFR_hierarchical(): Estimate the time-varying reported case fatality ratio (mu_jt) from WHO annual data
est_WASH_coverage(): Estimate, Visualize, and Impute WASH Coverage and Correlation with Cholera Incidence
est_demographic_rates(): Estimate Daily Demographic Rates from UN World Population Prospects Data
est_epidemic_peaks(): Estimate Epidemic Peaks from Cholera Time Series Data
est_immune_decay_vaccine(): Plot Vaccine Immune Decay
est_initial_E_I(): Estimate Initial E and I Compartments from Surveillance Data
est_initial_E_I_location(): Estimate E and I Compartments for a Single Location
est_initial_R(): Estimate Initial R Compartment from Historical Cholera Surveillance
est_initial_R_location(): Estimate Initial R Compartment for Single Location
est_initial_S(): Estimate Initial S Compartment from Other Compartment Priors
est_initial_V1_V2(): Estimate V1/V2 Initial-Condition Beta Priors from OCV Campaign History
est_kappa_prior(): Estimate Prior Distribution for Environmental Half-Saturation Constant (kappa)
est_mobility(): Fit Mobility Model Using Flight Data and Distance Matrices
est_nb_dispersion(): Estimate negative-binomial dispersion from surveillance observations
est_overland_tau_prior(): Build a per-country overland departure-rate prior for MOSAIC calibration
est_seasonal_dynamics(): Estimate Seasonal Dynamics for Cholera and Precipitation Using Daily Fourier Series
est_suitability(): Estimate Environmental Suitability (psi) for Cholera Transmission
est_symptomatic_prop(): Estimate and Visualize Symptomatic Proportion Data
est_vaccination_rate(): Estimate OCV Vaccination Rates
est_vaccine_effectiveness(): Estimate Vaccine Effectiveness Decay Parameters
est_zeta_1_prior(): Estimate Prior Distribution for Symptomatic Shedding Rate (zeta_1)
est_zeta_2_prior(): Estimate Prior Distribution for Asymptomatic Shedding Rate (zeta_2)
est_zeta_ratio_prior(): Estimate Prior Distribution for the Shedding Ratio (zeta_ratio = zeta_1 / zeta_2)
estimated_parameters: Estimated Parameters Inventory
evaluate_rolling_cv(): Post-hoc evaluation of a rolling-window forecast-validation artifact
extract_sampling_metadata(): Extract Sampling Metadata from Config
fit_beta_from_ci(): Fit Beta Distribution from Mode and 95% Confidence Intervals
fit_beta_safe(): Helper Function to Safely Fit Beta Distribution
fit_beta_with_variance_inflation_R(): Helper Function to Fit Beta Distribution with Variance Inflation for est_initial_R
fit_gamma_from_ci(): Fit Gamma Distribution from Mode and 95% Confidence Intervals
fit_gompertz_from_ci(): Fit Gompertz Distribution from Mode and Probability Interval
fit_lognormal_from_ci(): Fit Lognormal Distribution from Mode and 95% Confidence Intervals
fit_normal_from_ci(): Fit Normal Distribution from Mode and 95% Confidence Intervals
fit_truncnorm_from_ci(): Fit Truncated Normal Distribution from Mode and 95% Confidence Intervals
fit_uniform_from_ci(): Fit Uniform Distribution from Mode and 95% Confidence Intervals
fourier_series_double(): Fourier Series Model with Two Harmonics (Sine-Cosine Form)
generate_country_grid_dist(): Generate a Grid of Points Within a Country's Shapefile Based on Distance
generate_country_grid_n(): Generate a Grid of Points Within a Country's Shapefile Based on Number of Points
get_WASH_data(): Download, Process, and Save WASH Coverage Data
get_WHO_vaccination_data(): Get WHO Vaccination Data
get_cases_binary(): Create Binary Environmental Suitability Indicator from Case Data
get_cases_binary_from_peaks(): Create Binary Environmental Suitability Indicator from Epidemic Peaks
get_centroid(): Calculate the Centroid of a Country from its Shapefile
get_country_shp(): Get a country shapefile from GeoBoundaries API
get_default_config(): Create Default Simulation Configuration
get_default_subset_tiers(): Get Default Subset Tiers for Post-Hoc Optimization
get_distance_matrix(): Build distance matrix from longitude/latitude coordinates
get_effective_aic_range(): Get Effective AIC Range for Gibbs Temperature Scaling
get_elevation(): Get Mean or Median Elevation from Downloaded DEM Data Using Country Boundaries
get_feature_set(): Resolve a named feature set to a covariate vector
get_generation_time_distribution(): Estimate the Generation Time Distribution for Days and Weeks
get_ggplot_legend(): Extract the Legend from a ggplot Object
get_greek_unicode(): Get Unicode Hex Key for a Greek Letter
get_immune_decay_data(): Get Immune Decay Data
get_location_config(): Get Location-Specific Configuration
get_location_priors(): Get Location-Specific Priors
get_lstm_sequence_splits(): Create Train/Validation Splits for LSTM Sequences
get_param_names(): Get Parameter Names from MOSAIC Objects
get_paths(): Generate Directory Paths for the MOSAIC Project
get_python_paths(): Get Python Environment Paths
get_rho_care_seeking_params(): Estimate Beta Prior for Rho (Care-Seeking Rate) and Save Parameter Data Frame
get_suspected_cases(): Get Chi PPV Parameters and Save Parameter Data Frame
get_symptomatic_prop_data(): Get Symptomatic Proportion Data
get_travel_time_matrix(): Build an overland least-cost travel-time matrix between countries
get_vaccine_effectiveness_data(): Get Vaccine Effectiveness Data
get_who_region(): Get WHO Region for ISO Country Codes
grid_search_best_subset(): Grid Search for Best Subset with Early Stopping
impute_cyclone_probability(): Impute Country-Week Tropical-Cyclone Probability from EM-DAT and Climate
impute_drought_probability(): Impute Country-Week Drought Probability from a SPEI-Deficit Label
impute_flood_probability(): Impute Country-Week Flood Probability from EM-DAT and Climate Covariates
inflate_priors(): Inflate Prior or Posterior Distributions by a Variance Factor
install_dependencies(): Install R and Python Dependencies for MOSAIC
inv_logit(): Inverse Logit (Logistic) Transformation
iso_codes_africa: ISO3 Country Codes for All African Countries
iso_codes_africa_central: ISO3 Country Codes for Central African Countries
iso_codes_africa_east: ISO3 Country Codes for East African Countries
iso_codes_africa_north: ISO3 Country Codes for North African Countries
iso_codes_africa_south: ISO3 Country Codes for Southern African Countries
iso_codes_africa_west: ISO3 Country Codes for West African Countries
iso_codes_mosaic: ISO3 Country Codes for MOSAIC Modeling Framework
iso_codes_ssa: ISO3 Country Codes for Sub-Saharan African Countries
iso_codes_who_afro: ISO3 Country Codes for WHO AFRO Region Countries
list_mosaic_data_steps(): List the steps update_mosaic_data() knows about
load_or_install_packages(): Load or Install Required Packages (with GitHub handling for mobility, propvacc, and MOSAIC)
log_msg(): Simple Logging Function with Timestamps
logit(): Logit Transformation
make_forecast_cv_table(): Publication table for rolling-origin forecast cross-validation
make_lagged_data(): Create Lagged Versions of Data
make_mosaic_cluster(): Create a Reusable MOSAIC Parallel Cluster
make_mu_jt(): Build the daily reported-CFR matrix mu_jt from annual estimates
make_param_df(): Get Template for Parameter Values
make_simulation_config(): Create a Simulation Configuration File
mosaic_color_variant(): Create Lighter, Darker, or Muted Variants of a Color
mosaic_colors(): Get MOSAIC Semantic Colors
mosaic_control_defaults(): Build Complete MOSAIC Control Structure
mosaic_io_presets(): Get Pre-configured I/O Settings
mosaic_pal_countries(): Generate Colors for MOSAIC SSA Countries
mosaic_pal_discrete(): Generate a Qualitative MOSAIC Palette
mosaic_pal_diverging(): Generate a Diverging MOSAIC Palette
mosaic_pal_scenarios(): Generate a Scenario Comparison Palette
mosaic_pal_sequential(): Generate a Sequential MOSAIC Palette
mosaic_pal_uncertainty(): Generate Graduated Uncertainty Colors from a Base Color
mosaic_palette(): Generate a MOSAIC Color Palette
optimize_ensemble_subset(): Optimize Ensemble Subset Size
pgompertz(): Gompertz Distribution Cumulative Distribution Function
plot_CFR_by_country(): Plot Case Fatality Ratios, Total Cases, and Beta Distributions by Country
plot_CFR_hierarchical(): Plot Hierarchical CFR Model Results
plot_ENSO_data(): Plot ENSO and IOD Data
plot_Reff(): Plot the route-decomposed effective reproductive number over time
plot_africa_map(): Plot Africa Map with Cholera Outbreak Countries
plot_cases_binary(): Plot Binary Environmental Suitability Indicator with Case Data
plot_cholera_surveillance_data(): Plot Combined Cholera Surveillance Data
plot_climate_data(): Plot Weekly Climate Data for a Given Country and Save to File
plot_departure_tau(): Plot the model-implied departure probability (\(\tau_i\)) and daily travelers
plot_diffusion_pi(): Plot the model-implied diffusion connectivity matrix (\(\pi_{ij}\))
plot_effective_range(): Plot Effective Range Function
plot_epidemic_peaks(): Plot Epidemic Peaks from Detection Results
plot_forecast_cv_grid(): Publication timeseries grid for rolling-origin forecast cross-validation
plot_forecast_cv_skill(): Headline forecast-CV plot: per-country out-of-sample metric across origins
plot_generation_time(): Plot Generation Time as Gamma Distribution for Days and Weeks
plot_mobility(): Plot Mobility Data, Mobility Network, and Results
plot_mobility_flux_matrix(): Plot the model-implied daily mobility flux matrix (\(M_{ij}\))
plot_mobility_flux_network(): Plot the model-implied mobility flux network
plot_mobility_fused(): Publication figures for the fused overland OD connectivity model
plot_model_convergence_status(): Plot model convergence status table
plot_model_distributions(): Plot Multi-Method Parameter Distributions
plot_model_ensemble(): Plot Ensemble Predictions from a mosaic_ensemble Object
plot_model_likelihood(): Plot Model Likelihood Curve
plot_model_parameter_correlation(): Plot Posterior Parameter Correlation Heatmap
plot_model_parameter_sensitivity(): Plot Parameter Sensitivity Ranking (HSIC)
plot_model_parameters(): Plot Model Parameters vs Likelihood (Deprecated)
plot_model_posterior_quantiles(): Plot Model Posterior Quantiles
plot_model_posteriors_detail(): Plot Detailed Prior to Posterior Distributions
plot_model_ppc(): Plot Posterior Predictive Checks from Ensemble Predictions
plot_model_subset_optimization(): Plot Best-Subset Optimization Diagnostic from a mosaic_subset_optimization Object
plot_model_trajectories(): Plot comprehensive model trajectories for one location
plot_mosaic_country_map(): Plot MOSAIC Country Map
plot_mosaic_palette(): Display a Color Palette as Swatches
plot_psi_star_diagnostic(): Plot Raw vs Calibrated Environmental Suitability (psi vs psi*)
plot_recovery_duration(): Plot recovery durations and rates for symptomatic and asymptomatic infections
plot_rho_care_seeking_params(): Plot Rho (Care-Seeking Rate) Prior Estimation
plot_rolling_cv(): Presentation-quality plot of a rolling-origin forecast-validation artifact
plot_seasonal_clustering(): Plot Clustering of Countries Based on Seasonal Dynamics
plot_seasonal_transmission(): Plot Precipitation and Cholera Case Data with Fourier Series Fits
plot_seasonal_transmission_example(): Plot Seasonal Transmission Example with Grid Points
plot_shedding_rate(): Plot Shedding Rate as a Function of Environmental Suitability
plot_spatial_correlation_heatmap(): Heat-map of a spatial–correlation matrix (ggplot2)
plot_spatial_hazard(): Plot spatial importation hazard
plot_suitability_and_cases(): Plot Predicted Suitability and Reported Cholera Cases for a Specific Country
plot_suitability_by_country(): Plot Predicted Suitability for All Countries
plot_suspected_cases(): Plot Chi PPV Prior Distributions
plot_vaccination_data(): Plot Vaccination Data
plot_vaccination_maps(): Plot Vaccination Maps
plot_vaccine_effectiveness(): Plot Vaccine Effectiveness Decay and the Distributions Fitted to It
plot_vibrio_decay_rate(): Plot Vibrio Decay Rate as a Function of Environmental Suitability
prefit_rolling_cv_psi(): Pre-fit and freeze the environmental-suitability (psi) cache for rolling CV
print(<mosaic_initial_conditions_S>): Print method for S compartment initial conditions
print(<mosaic_priors>): Print summary of location priors
priors_default: Default prior distributions for MOSAIC model parameters
process_AI_cholera_data(): Process AI-mined Weekly Cholera Surveillance Data into the MOSAIC Per-Source Schema
process_CFR_data(): Process Cholera Data to Calculate Aggregated Case Fatality Ratios and Fit Beta Distributions
process_EMDAT_data(): Process EM-DAT Flood and Tropical-Cyclone Events Into Country-Week Panels
process_GTFCC_vaccination_data(): Process GTFCC Vaccination Data for MOSAIC Model
process_IDMC_data(): Process IDMC Internal Displacement Events Into Country-Week Panels
process_JHU_weekly_data(): Process Weekly Cholera Data from JHU: Convert to MOSAIC Format
process_OAG_data(): Process and Save OAG Flight Data for Africa
process_SUPP_weekly_data(): Process supplementary cholera outbreak data from raw SUPP directory
process_UNICEF_malnutrition_data(): Process UNICEF Child Malnutrition Data
process_UN_demographics_data(): Process UN World Prospects Data for African Countries
process_WB_GDP_data(): Process World Bank GDP data
process_WB_population_density_data(): Process World Bank Population Density Data
process_WB_poverty_ratio_data(): Process World Bank Poverty Ratio Data
process_WB_urban_population_data(): Process World Bank Urban Population Proportion Data
process_WHO_annual_data(): Download, Process, and Save Historical Annual WHO Cholera Data for Africa
process_WHO_vaccination_data(): Process Vaccination Data for MOSAIC Model
process_WHO_weekly_data(): Process Weekly Cholera Data: Convert Country Names to ISO3 and Filter by WHO AFRO Region
process_cholera_surveillance_data(): Process Combined Weekly and Daily Cholera Surveillance Data with Truly Square Data Structure
process_country_similarity_data(): Process Country Similarity Data for African Countries
process_enso_data(): Process ENSO Data from enso-data Repository
process_mobility_od_data(): Fuse four bilateral mobility sources into a connectivity structure
process_open_meteo_data(): Process Open-Meteo Climate Data from open-meteo-pipeline Repository
qgompertz(): Gompertz Distribution Quantile Function
rake_mobility_od_to_tau(): Rake the fused OD structure to per-country outbound departure margins
read_hdf5_to_list(): Read an HDF5 File into an R List
read_json_to_list(): Read a JSON File into an R List
read_model_json(): Read priors or posteriors from JSON with consistent parsing
read_yaml_to_list(): Read a YAML File into an R List
refresh_data_repos(): Pull latest data from external scraper repos and report coverage
remove_python_env(): Remove the MOSAIC Python Environment
render_MOSAIC_figures(): Render all MOSAIC figures from a finished run directory
rgompertz(): Generate Random Gompertz Variates
run_MOSAIC() run_mosaic(): Run MOSAIC Calibration Workflow
run_WHO_annual_data_app(): Run Shiny Application for Visualizing WHO Annual Cholera Data in AFRO Countries
run_fit_sandbox(): Deterministic Fit-Diagnostic Sandbox: One Simulation with Parameter Overrides
run_rolling_cv(): Rolling-Window Forecast Validation for the MOSAIC Transmission Model
run_simulation(): Run the cholera transmission model
sample_from_prior(): Sample from Prior Distribution (Simplified)
sample_parameters(): Sample Parameters from Prior Distributions
scale_color_mosaic_c() scale_colour_mosaic_c() scale_fill_mosaic_c(): MOSAIC Sequential Color/Fill Scales for ggplot2
scale_color_mosaic_d() scale_colour_mosaic_d() scale_fill_mosaic_d(): MOSAIC Discrete Color/Fill Scales for ggplot2
scale_color_mosaic_div() scale_colour_mosaic_div() scale_fill_mosaic_div(): MOSAIC Diverging Color/Fill Scales for ggplot2
scale_color_mosaic_scenario() scale_colour_mosaic_scenario() scale_fill_mosaic_scenario(): MOSAIC Scenario Color/Fill Scales for ggplot2
set_root_directory(): Set Root Directory
log_info() log_warn() log_error() log_fatal(): Severity-tagged logging helpers
store_sampling_metadata(): Store Sampling Metadata in Config
theme_mosaic(): MOSAIC ggplot2 Theme
update_mosaic_data(): Refresh every MOSAIC data input that can be refreshed automatically
update_priors_from_posteriors(): Update Priors from Posteriors for Staged Estimation
use_mosaic_env(): Use MOSAIC Python Environment
validate_priors_json(): Validate priors JSON structure
validate_sampled_config(): Validate Sampled Config
weighted_quantiles(): Weighted quantiles
weighted_quantiles_presorted(): Weighted quantiles from already-sorted, pre-filtered inputs
weighted_var(): Weighted Statistical Functions for Posterior Analysis
write_json_or_gz(): Write a list to .json, .json.gz, or both – byte-equal by construction
write_list_to_hdf5(): Write an R list to an HDF5 file
write_list_to_json(): Write an R List to a JSON File
write_list_to_yaml(): Write an R List to a YAML File
write_model_json(): Standardized JSON I/O utilities for MOSAIC model files
write_trajectory_csv(): Write ensemble trajectory channels to per-location CSV